Date of Award:

12-2021

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Special Education and Rehabilitation Counseling

Committee Chair(s)

Sandra Gillam

Committee

Sandra Gillam

Committee

Ronald Gillam

Committee

Lisa Milman

Committee

Sarah Schwartz

Committee

Tyson Barrett

Abstract

The purpose of the study was to investigate the feasibility of streamlining the transcription and scoring portion of language sample analysis (LSA) through computer-automation. LSA is a gold-standard procedure for examining childrens’ language abilities that is underutilized by speech language pathologists due to its time-consuming nature. To decrease the time associated with the process, the accuracy of transcripts produced automatically with Google Cloud Speech and the accuracy of scores generated by a hard-coded scoring function called the Literate Language Use in Narrative Analysis (LLUNA) were evaluated. A collection of narrative transcripts and audio recordings of narrative samples were selected to evaluate the accuracy of these automated systems. Samples were previously elicited from school-age children between the ages of 6;0-11;11 who were either typically developing (TD), at-risk for language-related learning disabilities (AR), or had developmental language disorder (DLD). Transcription error of Google Cloud Speech transcripts was evaluated with a weighted word-error rate (WERw). Score accuracy was evaluated with a quadratic weighted kappa (Kqw). Results indicated an average WERw of 48% across all language sample recordings, with a median WERw of 40%. Several recording characteristics of samples were associated with transcription error including the codec used to recorded the audio sample and the presence of background noise. Transcription error was lower on average for samples collected using a lossless codec, that contained no background noise. Scoring accuracy of LLUNA was high across all six measures of literate language when generated from traditionally produced transcripts, regardless of age or language ability (TD, DLD, AR). Adverbs were most variable in their score accuracy. Scoring accuracy dropped when LLUNA generated scores from transcripts produced by Google Cloud Speech, however, LLUNA was more likely to generate accurate scores when transcripts had low to moderate levels of transcription error. This work provides additional support for the use of automated transcription under the right recording conditions and automated scoring of literate language indices. It also provides preliminary support for streamlining the entire LSA process by automating both transcription and scoring, when high quality recordings of language samples are utilized.

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